Metrology method and system

ABSTRACT

A metrology method for use in determining one or more parameters of a patterned structure, the method including providing raw measured TEM image data, TEM meas , data indicative of a TEM measurement mode, and predetermined simulated TEM image data including data indicative of one or more simulated TEM images of a structure similar to the patterned structure under measurements and a simulated weight map including weights assigned to different regions in the simulated TEM image corresponding to different features of the patterned structure, performing a fitting procedure between the raw measured TEM image data and the predetermined simulated TEM image data and determining one or more parameters of the structure from the simulated TEM image data corresponding to a best fit condition.

TECHNOLOGICAL FIELD AND BACKGROUND

The present invention is generally in the field of metrology techniquesfor measuring various parameters of patterned structures, such assemiconductor wafers, and can be used for example for controlling one ormore processes in the manufacture of such structures.

Semiconductor structures, such as integrated circuits, become morecomplicated in the dimensions and shapes of pattern features, as well asintegrate thinner and multi-stack films of novel material compositions(e.g. SiGe, HKMG, 3D FinFet, etc.). Accordingly, there exists anincreasing need in providing accurate measurements of full 3-dimensionalstructures, and in enabling these measurements to be applied tostructures progressing on a production line, i.e. automatic inspection(metrology, defect detection, process control, etc.) of patternedstructures.

Various metrology/inspection techniques have been developed. Some ofthem utilize model-based interpretation of measured data to extractparameters of the structure being measured. The increasing complexity inpatterns and material compositions of patterned structures require morecomplex models for interpretation of measured data. To this end, modeloptimization techniques have been developed utilizing, for example, ahybrid approach and/or measured data from reference system(s).

Hybrid Metrology is the practice of combining two or more metrologytools that measure the same or similar structures. Data is sharedbetween toolsets in complementary way to enhance metrology performance,and enables measurement of complex structures that cannot be measuredwith enough performance by any of the individual toolset. Variousexamples of a hybrid approach are described in WO 11/158239, WO14/102792 WO 15/125127, WO 17/021968, all assigned to the assignee ofthe present application.

As indicated above, interpretation of measured data may be assisted bycomparison—or model optimization by structure parameter(s) measured by areference system. One of the commonly used, high-accuracy, referencemeasurement system is based on Transmission Electron Microscopy (TEM).TEM is known to allow imaging of extremely small features, on the orderof nanometers. With the decrease of pattern features in semiconductordevices, TEM is widely used for monitoring the manufacturing process. Incontrast to SEM, which only images the surface of a material, TEM allowsalso to analyze the internal structure of a sample. In a TEM, a broadbeam impacts the sample and electrons that are transmitted through thesample are focused to form an image of the sample. In a scanningtransmission electron microscope (STEM), a primary electron beam isfocused to a fine spot, and the spot is scanned across the samplesurface. Electrons that are transmitted through the substrate arecollected by an electron detector on the far side of the sample, and theintensity of each point on the image corresponds to the number ofelectrons collected as the primary beam impacts a corresponding point onthe surface.

GENERAL DESCRIPTION

There is a need in the art for a novel metrology technique for measuringin patterned structures (e.g. a periodical semiconductor device array,e.g. logic, memory) utilizing image data obtained by a TransmissionElectron Microscopy (TEM) or Scanning Transmission Electron Microscopy(STEM). The measurement results may be used for automatic processcontrol (APC), e.g. during semiconductor manufacturing.

TEM/STEM is a microscopy technique in which a beam of electrons istransmitted through a very thin section or slice (also termed“Lamellae”) of a structure, and interaction of the electrons withfeatures of the structure within the Lamellae, forms an image of saidLamellae. TEM is known to provide high-accuracy measurements which arerequired for measuring in complex patterned structures (complex patternsand material compositions).

TEM-based measurements are relatively slow (as compared to opticalones), and are typically performed at a stand-alone station, which maybe part of a FAB production line. According to the conventionalapproach, TEM images are 2D images, which are processed to finddistances and angles between edges in TEM images, utilizing imageprocessing algorithms based on pattern recognition and pre-definedrecipes. Thus, the known methods of data analysis of TEM images providesome mixed information about the Patterned Structure features withinLamellae being imaged, and practically cannot provide full geometrical(dimensional) and/or material information about a structure within theLamellae image. These methods are neither robust nor accurate in thecase of a structure (e.g. transistor device).

The present invention, according to one of its aspects, provides a novelsystem and method for automatic robust and accurate retrieval ofgeometric and/or material-related parameters of structures from one orseveral TEM images. In this connection, it should be noted that theprinciples of the invention can be applied to TEM data obtained with orwithout scan-mode measurements. Therefore the term “TEM” used hereinbelow should be interpreted broadly covering also STEMtool/measurements.

The technique of the invention provides novel model-based interpretationof image data obtained by TEM, to provide full geometrical (and possiblealso material) interpretation of the TEM data. It should be understoodthat TEM image data is not a “real image” or “picture”, but pixel mapdata, which is an intensity map based on detected transmission ofelectrons through the structure, defined by the physics of TEM imagingnamely such parameters as electron beam energy, type and configurationof the detector, and geometry and material composition of the structure.

The invention utilizes position data of a measurement slice (Lamellaesample) with respect to a structure under measurements, considering aparametrized 3D model of features (e.g. dimensional parameters) of thestructure, and possibly also material compositions' properties of thestructure. Also, the invention utilizes weighting approach based onassignment of weights (weighting factors) to different regions in asimulated TEM image connected to (corresponding to) predefined differentfeatures/parts of the patterned structure being measured.

As described above, according to the conventional approach, 2D TEMimages are processed to find distances and angles between edges in theTEM images, utilizing image processing algorithms based on patternrecognition and pre-defined recipes. The present invention is based onthe inventor's understanding that appearance of the edges-related andother features in a TEM image is strongly connected to the exactLamellae position and thickness relative to the structure. This, as wellas other factors, such as low contrast appearance of some features in aTEM image, prevents the conventional approach from being sufficientlyrobust.

More specifically, as will be described further below, the inventionutilizes an accurate 3D model-based matching (fitting) procedure whichtakes into account position uncertainty and has superior robustness ifsome of the features have low contrast. Taking into account physicalconstraints (from knowledge of semiconductor manufacturing process andtrends), the physical stability of obtained results is assured androbustness of the analysis is improved.

Based on the 3D model of a structure (e.g. 3D geometry and materialproperties) and the physics of the specific TEM (or STEM) measurementmode, and possibly also a detection scheme, a simulated(expected/modeled) TEM image data is determined/calculated. Thedetection scheme, for example, may be Bright Field (BF), Dark Field(DF), Annular Dark Field (ADF), High Angle Annular Dark Field (HAADF),Energy Loss (EL), and others. The simulated TEM image data includes oneor more simulated TEM images and a simulated weight map (image ofweights).

It should be understood that some parameters of the structure havestronger effect on a TEM image (both measured and simulated/modeledimage), while other parameters have weaker influence on TEM image. The“relatively strong” parameters can be extracted from the TEM image withbetter accuracy and robustness (e.g. using regression). The robustmeasurement of “relatively weak” parameters is a more challenging taskbecause they may be significantly affected both by noise at the TEMimage (random noise, discoloration of different parts, defects,non-periodicity of measured device) and by inaccuracies in model ormeasurements of relatively strong parameters.

Such relatively strong and weak parameters will be referred tohereinbelow as, respectively, strong and weak parameters, meaning thatvalues of such parameters or change in values of such parameters haverelatively strong and weak effects, respectively on the TEM image. Theparameters of interest (POIs) or target parameters (TPs) can includeboth strong and weak parameters. Sometimes the most important parameteris a weak parameter, which is challenging to measure.

It should be noted that, for the purposes of the present disclosure,data indicative of the TEM transmission mode includes Lamellae positiondata with respect to the structure. The theoretical TEM image data isindicative of a 2D array of intensities, where different positions inthe array correspond to different positions within the Lamellaeprojection.

The 3D model (geometry and material properties) of the structure (forexample, transistors) may be provided using any known suitabletechnique, for example obtained by OCD measurements and created based onexperience and knowledge of semiconductor manufacturing, e.g. NovaMARS®software (application development platform) commercially available fromNova Measuring Instruments. Israel. Such 3D model of a structureincludes parametrization, which allows to describe process variation inthe manufacturing process. It should be understood that selected 3Dmodel is to be sufficient to describe the normal and common abnormalprocess variations, and, on the other side, a number of adjustable(floating) parameters in the model is to be as small as possible.

Thus, according to one broad aspect of the invention, there is provideda control system for use in measuring one or more parameters of apatterned structure. The control system is configured as a computersystem comprising: an input utility configured to receive input datacomprising raw measured TEM image data, TEM_(meas), and data indicativeof a TEM measurement mode; and a data processor configured to processthe raw measured TEM image data, TEM_(simul), and generate output dataindicative of one or more parameters of a patterned structure. The dataprocessor comprises an optimization module configured and operable toutilize said data indicative of the TEM measurement mode and perform afitting procedure between the raw measured TEM image data, TEM_(meas),and predetermined simulated TEM image data, TEM_(simul) and determiningone or more parameters of the structure from the simulated image datacorresponding to a best fit condition. The predetermined simulated TEMimage data, TEM_(simul) is based on a parametrized three-dimensionalmodel of features of the patterned structure, and comprises one or moresimulated TEM images and simulated weight map comprising weightsassigned to different regions in the simulated TEM image correspondingto different features of the patterned structure. according to apredefined structure's map data.

The parameter(s) of the structure to be determined may includedimensional parameters, such as for example one or more of thefollowing: Critical Dimensions (CD) of the pattern features, layer(s)'thickness(es), Side Wall Angle (SWA), Pitch Walking, etc.; as well asmaterials related parameters/properties and variation of theseparameters (e.g. within Lamellae).

The parametrized 3D model of the structure's features includesgeometrical features (dimensions of the features) of the structure, andin some embodiments, may also include material-related data (materialproperties).

The optimization module may be configured and operable to perform thefitting procedure while varying one or more of the following: one ormore of the three-dimensional model (e.g. 3D model) parameters; one ormore of material properties; and Lamellae position with respect to thethree-dimensional model of the structure; and while dynamicallyre-calculating the weight map by varying/re-assigning the weights to thedifferent regions of the simulated TEM image, depending on the Lamellaegeometry and position relative to the predetermined differentfeatures/parts of the patterned structure.

In some embodiments, the control system may be configured to access astorage device and receive therefrom the predetermined simulated TEMimage data, TEM_(simul). Alternatively or additionally, the dataprocessor further comprises a data simulator module, which is configuredand operable to analyze data indicative of the parametrizedthree-dimensional model of geometrical features of the patternedstructure, and create the predetermined simulated TEM image data,TEM_(simul). The simulated TEM image data may include a plurality ofsimulated TEM images for multiple different Lamellae positions andgeometries with respect to the three-dimensional model of the patternedstructure.

The control system may be configured for data communication with one ormore measured data providers to receive therefrom the raw measured TEMimage data, TEM_(meas), comprising data indicative of at least one TEMimage.

The data indicative of the three-dimensional model of the patternedstructure may further comprises material related data of the structure.

The data indicative of the one or more parameters of the TEM measurementmode comprises Lamellae geometry data. The latter may include one ormore of the following: Lamellae thickness, Lamellae orientation,Lamellae position with respect to the structure.

The data indicative of the one or more parameters of the TEM measurementmode may further comprise data about a detection scheme corresponding tothe measured TEM image data.

The control system further comprises a parameter calculator moduleconfigured and operable to provide, from the simulated TEM image datacorresponding to the best fit condition, the one or more parameters ofthe patterned structure being measured. The parameter calculator modulemay be configured and operable to provide, from the simulated TEM imagedata corresponding to the best fit condition, the full geometricalinterpretation of the TEM image data.

The control system may further comprise an analyzer configured to usethe one or more parameters of the patterned structure determined fromthe TEM image data to optimize measured data corresponding tomeasurements of a type different from TEM, obtained on the patternedstructure. The analyzer may comprise a modelling utility configured andoperable to use the one or more parameters of the patterned structuredetermined from the TEM image data and optimize measured datainterpretation model for said measurements of the type different fromTEM. Such different type measured data may include OCD measured dataand/or XRS data and/or XRF data.

The invention according to its another broad aspect provides a metrologymethod for use in determining one or more parameters of a patternedstructure, the method comprising: providing raw measured TEM image data,TEM_(meas), and data indicative of a TEM measurement mode, andpredetermined simulated TEM image data comprising data indicative of oneor more simulated TEM images of a structure similar to the patternedstructure under measurements and a simulated weight map comprisingweights assigned to different regions in the simulated TEM imagecorresponding to different features of the patterned structure accordingto a predefined structure's map; performing a fitting procedure betweenthe raw measured TEM image data and said predetermined simulated TEMimage data, and determining one or more parameters of the structure fromthe simulated TEM image data corresponding to a best fit condition.

As described above, the parameters of interest (target parameters) ofthe structure to be determined can include both strong and weakparameters, and, moreover, sometimes the most important parameter is aweak parameter, which is challenging to measure. The present inventionprovides for improving the ability of measurement of weak parameters bycreating trade-off between the accuracies of measurements of strong andweak parameters. Typically, the accuracy of measurement of strongparameters is very good, and may even be much better than required byspecification. On the other hand, the accuracy of measurement of weakparameters might be not sufficient.

The technique of the invention performs leverage of the accuracies ofmeasurements of strong and weak parameters by applying weights(weighting factors/coefficients) on different areas/regions of thesimulated TEM image. Such a weighted area/region in the simulated TEMimage is connected to/associated with a predetermined/selected(previously defined) feature(s)/part(s) of the patterned structure beingmeasured. These parts/features of the patterned structure may forexample be some specific elements/part(s) of a semiconductor devicedefined by the patterned structure (integrated structure), such as Gateat FinFET transistor, or SiGe L1 at FinFET transistor.

In other words, according to the invention, the importance of differentareas/regions in the TEM image can be adjusted (by weight assignedthereto) in order to get a desired leverage between accuracies ofmeasurement of weak and strong parameters of interest. Thus, differentweights are dynamically assigned to specific areas/regions of thesimulated TEM image which are connected to specific features/parts ofthe measured structure. These areas/parts are predefined (during recipecreation) based on the behavior of a measured structure, and not “fixed”on the TEM image. Depending on how Lamellae is cut and positionedrelative to the regions of interest in the patterned structure, theanalyzer (algorithm) automatically assigns different weights todifferent regions of the TEM image.

According to another aspect of the invention, it provides a novel hybridapproach, according to which TEM data is used for optimizing modeling ofor measurements by a type different from TEM (e.g. OCD), or vice versa,resulting in enhanced performance of either one or both of the TEM anddifferent type metrology measurements that cannot be obtainedseparately.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand the subject matter that is disclosedherein and to exemplify how it may be carried out in practice,embodiments will now be described, by way of non-limiting example only,with reference to the accompanying drawings, in which:

FIG. 1A is a block diagram of a control system of the invention for usein determining various structure parameters from TEM image data;

FIG. 1B is a block diagram more specifically illustrating the componentsof the exemplary embodiment of the system of FIG. 1A;

FIGS. 2A to 2D schematically illustrate the principles of the inventionfor generating simulated TEM image data, wherein FIG. 2A shows a “TEMstack” as a multi-parameter function of structure geometry and materialrelated data, and Lamellae geometry; FIGS. 2B and 2C exemplify apatterned structure undergoing TEM imaging and parameters describingLamellae geometry; and FIG. 2D schematically illustrates TEM imagingschemes and Lamellae cuts around the z-axis for BF, ADF and HAADFdetection schemes;

FIG. 3 is a flow diagram of an exemplary method of the present inventionfor obtaining full geometrical interpretation of TEM measurements fromraw TEM image data;

FIG. 4 is a flow diagram exemplifying an optional step of the method ofFIG. 3 preliminary TEM image analysis (rough positioning); and

FIGS. 5A to 5C exemplify creation and use of a weight map as part ofsimulated TEM data, wherein FIG. 5A shows an exemplary patternedstructure defining a typical FinFET SiGe transistor havingfeatures/parts of two different types (presenting relatively strong andweak parameters with respect to TEM imaging); FIG. 5B shows a simulatedTEM image for a theoretical structure similar to the patterned structureof FinFET SiGe transistor, showing the areas in the simulated TEM imagecorresponding to the features/parts of one type associated withrelatively weak parameter; and FIG. 5C illustrates simulated weight mapwith areas of enhanced weights which correspond to said “weak-parameter”features.

DETAILED DESCRIPTION OF EMBODIMENTS

Reference is made to FIG. 1A illustrating, by way of a block diagram, acontrol system 10 according to the present invention. The control system10 is configured and operable for analyzing measured data comprising rawTEM image data and predetermined simulated TEM image data, and determinethe structure parameter(s). The system may also provide for determininga full geometrical (and possibly also material composition) informationabout a structure being measured. To this end, as shown in the figure,the control system 10 is associated with a measured data provider 12.The measured data provider may be a TEM measurement system (real time oron line operation mode of control system 10) and/or or an externalstorage, where the TEM measured data is stored and to which the controlsystem 10 is connectable (e.g. via wireless signal communication using acommunication network). It should be understood that configuration andoperation of the TEM measurement system providing the TEM image data areknown per se and do not form part of the present invention, andtherefore need not be specifically described here. It should also benoted that the analysis of the TEM measured data by the control system10 may be at least partially performed off-line.

The control system 10 is configured as a computer system having, intercilia, such main structural and functional utilities (hardware and/orsoftware utilities) as data input and output utilities 10A, 10B, memory10C, and data processor 10D. The data processor 10D is configured (i.e.preprogrammed) for processing and analyzing measured data comprising rawmeasured TEM image data, TEM_(meas), and predetermined simulated TEMimage data, as well as data indicative of a TEM measurement mode, anddetermining the structure parameter(s).

Thus, the control unit 10 is configured to receive, from the measureddata provider 12, input data indicative of the raw measured TEM imagedata, TEM_(meas). The raw measured TEM image data may include a singleor set of several TEM or STEM images. As indicated above, TEM image isactually pixel map data, or intensity map data (2D array of intensities)based on transmission of electrons through a portion of the structurebeing measured/imaged (Lamellae) as detected by a pixel matrix of adetector. Different positions (pixels) in the array correspond todifferent positions on the Lamellae projection, as will be describedmore specifically further below.

Further, as shown in the figure, the control system 10 may be configuredto receive from a storage device 14 (a library) input data indicative ofpredetermined simulated TEM image, TEM_(simul). These simulated TEMimage data is based on a parametrized three-dimensional model offeatures of the patterned structure, and includes one or more simulatedTEM images and simulated weight map comprising weights assigned todifferent regions in the simulated TEM image corresponding to predefineddifferent features/parts of the patterned structure.

Alternatively, or additionally, although not specifically shown, thecontrol system 10 may include a suitable 3D model creator utility/moduleto provide such 3D model data. The 3D model data may be processed/usedby the control system 10 to create data indicative of one or moresimulated TEM images.

As indicated above, the 3D model data includes a geometrical model ofthe structure, and may also include material-related model data.Preferably, the 3D model includes both the 3D geometry and materialcompositions/properties. The 3D model of the structure is built bytaking into account the physical constraints (from knowledge ofsemiconductor manufacturing process and trends), and includesparametrization, which allows to describe process variation in themanufacturing process.

In this connection, it should be noted that techniques of 3D modeling ofa patterned structure are known, and do not form part of the presentinvention. An example of such modeling technique is NovaMARS® productcommercially available from Nova Measuring Instruments. Such model is,for example, used for interpretation of Optical Critical Dimension (OCD)measurements.

According to the invention, the structure-related data (3D model data)also includes data about different features/parts of the structure (i.e.device defined by the patterned structure) and their “importance” in aTEM image. As described above, these parts of the structure arepredefined (during recipe creation) based on the behavior of themeasured device/structure (not “fixed” on TEM image), and are used tocreate the simulated weight map data.

Also utilized by the control unit 10 is input data comprisinginformation about a TEM mode used to obtain the measured TEM image data.Such TEM mode data could include data indicative of at least one of ameasurement mode/scheme, including for example Lamellae related data(position data, angular position, dimension, etc.), measurementcondition data (applied metrology technique parameters effecting signalabsorption, e.g. dark/bright field, angle, scale (magnification), etc.).Also, such data may include the measured structure related data (e.g.geometry and/or materials, etc.).

For example, Lamellae related data may include information about howLamellae was cut and positioned relative to different predefined partsof the patterned structure. This data is used to assign differentweighting factors (different weights) to the corresponding differentareas/regions of the TEM image.

More specifically, the Lamellae position data may include an approximateposition of the Lamellae center relative to the measured structure,angular orientation. Lamellae thickness. According to another embodimentof the invention, the position of the Lamellae center is not requiredand is determined automatically during the Rough Positioning step ofdata analysis. This will be described more specifically further below.

The data processor 10D receives the input data (via the memory 10C)including 3D model data about the structure, TEM mode data, and measuredTEM image data. The data processor 10D includes an optimizationutility/module (including a fitting module 18) which is configured andoperable to utilize said data indicative of the TEM measurement mode andperform a fitting procedure between the raw measured TEM image data,TEM_(meas), and the predetermined simulated TEM image data, TEM_(simul)and determine the simulated image data corresponding to a best fitcondition to thereby enable determination therefrom (by a parameters'calculator) one or more parameters of the structure.

As schematically illustrated by the block diagram of FIG. 1B, in someembodiments, the data processor 10D also includes a TEM image datasimulator 16. The TEM image simulator 16 is configured and operable toreceive the TEM mode data and 3D model data and generate simulated 2DTEM image data, TEM_(simul), using the 3D model of the structure andtaking into account the TEM imaging parameters/conditions (TEM mode).The simulated 2D TEM image data is thus generated (i.e. bytransformation of 2D TEM image onto a 3D model of the structure), whichtakes into account Lamellae position uncertainty. The simulated 2D TEMimage data includes the simulated TEM image(s) and weights' image(weight map). This will be described more specifically further below.

The optimization/fitting module 18 receives the measured TEM image data,TEM_(meas), and receives, from the simulator module 16 or from thelibrary, the simulated TEM image(s) TEM_(simul), and performs a fittingprocedure.

The fitting procedure is an iterative procedure of comparing between thesimulated image data TEM_(simul) and measured data TEM_(meas), whilevarying the floating parameters of the simulated data (e.g. structureparameters, Lamellae geometry and position data, and also the weightingfactors defining a weight map for each iteration), until a best fitcondition is achieved. In other words, registration between thesimulated and measured images is performed to match the relativeposition, and a matching score (merit function or target function) iscalculated until the minimal value thereof is obtained, whichcorresponds to the best fit condition of the simulated data. The bestfit condition data for the simulated image data TEM_(simul) actuallydescribes/corresponds to the specific Lamellae image/projection in thestructure being measured. This best-fit simulated TEM image data is thenused by the parameters' calculator module 20 to determine one or moreparameters of the structure within the Lamellae portion.

It should be understood that multiple simulated TEM images can becreated for various “theoretical” Lamellae conditions (positions,dimensions, etc.) and TEM modes, for the same modeled structure, to forma library of simulated TEM image data. Generally speaking, the simulatormodule 16, if any, may be an external utility to which the fittingmodule 18 has access to receive the simulated data. The fittingprocedure between measured and simulated data may be repeated forvarious Lamellae positions and the weight maps (during TEM scan) toenable the full geometrical interpretation of the TEM image data, or aso-called “TEM stack” data.

The structural parameter(s) may be output, via output utility 10B, to afurther analyzer (not shown) to be analyzed using any known suitabletechnique to evaluate the respective parameters/conditions of astructure manufacturing process to modify/adjust the process parameters.

Alternatively, or additionally, as shown in FIG. 1 by dashed lines (asbeing an optional procedure), the so-obtained parameters of thestructure may be input into an analyzer 21, which is configured andoperable for measured data analysis of measurements of a different type(i.e. based on different physical principles, e.g. spectrometrymeasurements). It should be noted that such analyzer 21 may beconfigured to perform model-based or model-less data analysis. In thepresent not limiting example, a model-based configuration of theanalyzer 21 is shown.

The analyzer 21 includes a modeling utility 22 for optimizing datainterpretation model(s). This may be data interpretation model foranother metrology technique, e.g. OCD technique. In this case, thecontrol system 10 (the data processor 10D) may include a fitting module24 which receives measured OCD data, OCD_(meas), from the same measureddata provider 12 or a separate data provider 12′, and utilizes theoptimized OCD data interpretation model to perform a fitting procedureuntil a best fit condition is achieved, which data is then used by acalculator module 26 to determine one or more structure parameters fromthe OCD measurements.

Reference is now made to FIGS. 2A, 2B, 2C, and 2D schematicallyillustrating the principles of operation of the TEM image simulatormodule 16. As indicated above, the TEM image simulator module 16generates data indicative of 2D simulated/expected TEM image data, whichtakes into account Lamellae position uncertainty, and a weight map(image of weights). More specifically, the expected 2D TEM image issimulated (calculated) from the 3D model of the structure and thephysics of the specific TEM/STEM measurement mode, using an appropriatemodeling software application. The weigh map is simulated based on thepredefined (e.g. during recipe creation) different portions(features/parts) of the patterned structure (structure parts' map) andrespective areas/regions in the simulated TEM image.

As illustrated in FIG. 2A, the TEM image data simulation is based on theunderstanding that TEM image 30 (so-called “TEM stack” considering amulti-layer patterned structure, which is typically the case insemiconductor industry) is a multi-parameter function of suchfactors/data as the structure (stack) geometry 32, material related dataof the structure 34, Lamellae geometry and relative location in thestructure 36 (Lamellae cut), and TEM measurement mode data 38, andpossibly also the structure parts' map 39. The stack geometry data 32includes the geometrical parameters in the 3D model format and/or numberof repeated stack periods (along one or two axis across thestructure/stack). The material related data 34 is indicative of materialproperties collection (parameters), and includes material (type, name,etc.) and/or absorption value (or range of values).

As described above, the TEM measurement mode may also include adetection scheme, e.g. Bright Field (BF), Dark Field (DF), Annular DarkField (ADF), High Angle Annular Dark Field (HAADF), Energy Loss (EL),and others. In this connection, reference is made to FIG. 2Dschematically illustrating TEM imaging modes and corresponding Lamellaecuts around the z-axis for BF, ADF and HAADF detection schemes.Different detection schemes might require the use of different modelsfor measured data simulation. For example, in the ADF and HAADFdetection schemes, similar geometrical models and differentmaterial-based models can be used.

FIG. 2B exemplifies a patterned structure (stack) 40, undergoing TEMimaging with a certain geometry/position of Lamellae 42 with respect tothe structure 40. The parameter(s) of the patterned structure to bedetermined may include dimensional parameters, such as for example oneor more of the following: Critical Dimensions (CD) of the patternfeatures, layer(s)'thickness(es), Side Wall Angle (SWA), etc.; as wellas materials related parameters/properties.

As shown in FIG. 2C, the Lamellae geometry/position data 36 includes oneor more of the following parameters: horizontal length L_(hor), verticallength L_(ver), width (thickness) as well as a width step forcalculation, angular position around z-axis (with the initial positionperpendicular to the x-axis (angle X=0), horizontal shift S_(hor),vertical shift S_(ver), and in depth shift S_(depth).

Referring to FIG. 3 , there is illustrated a flow diagram 50 of anexemplary method of the present invention, e.g. carried out by the abovedescribed system 10 of FIGS. 1A and/or 1B, for obtaining parameter(s) ofthe structure, e.g. including full geometrical interpretation of TEMmeasurements from raw TEM image data indicative of one or more TEMimages. As shown in the figure in a self-explanatory manner, input datais provided (step 52) in a manner described above, i.e. from measureddata provider(s) including a measurement system and/or separate storagedevice. The input data includes: raw TEM image data, T_(meas), 3D modeldata of a patterned structure, TEM_(mode) data (physics of TEM images,as described above), and TEM material properties related data (includingdata indicative of electron beam interaction with the materials), andstructure-related data including the predefined structure parts' map. Inthe present non-limiting example, the 3D model data is the NovaMARSmodel.

The 3D Model(s) of the structure and data about various conditions ofthe TEM imaging mode are processed to generate 2D simulated TEM image(s)or TEM signatures, and simulated weight map (step 54). In thisconnection, it should be noted, that the present non-limiting exampleillustrates Real Time Regression (RTR) approach, according to whichsimulated date is generated in an “on-line” mode. As described above, alibrary (database of simulated data) for simulated TEM image(s) can beprepared, based on the 3D model, in an off-line mode, and used later on(e.g. in the on-line mode) for the optimization, i.e. fitting withmeasured data. In yet another example, in addition to real-time andlibraries based techniques, a combined approach may be used. Forexample, some “sparse” library may be created and after fitting withsuch library, further fitting is done based on the RTR approach.

The simulated TEM signature(s) and simulated weight map then undergo an“optimization” procedure (step 56). In this connection, as shown in thefigure in dashed lines, prior to the optimization step 56, an optionalrough positioning step 55 may be performed, i.e. preliminary TEM (orSTEM) image analysis.

As exemplified by a flow diagram 70 in FIG. 4 , such rough positioningmay be performed as follows. A set of simulated TEM images is calculated(step 72). More specifically, a fitting procedure is performed (Lamellaeposition adjustment, as shown in FIG. 2C). If it appears that Lamellaeorientation is not matched to periodicity axes (“non-zero angle” lateron, which means that the angle between Lamellae and x-axis is differentfrom 0, 90, 180 or 270 degrees), then such set may include a single wideimage or a set of wide images with different shifts in directionperpendicular to the Lamellae surface. The “wide” simulated image meansthat the width of the simulated image is significantly larger (severaltimes) than the width of the measured TEM image. For “non-zero” anglethe periodicity of the device pattern is broken by the Lamellae cutorientation, so different parts of wide image (in horizontal direction)may correspond to different sections of the structure. If Lamellaeorientation is matched to the periodicity axes (“zero angle” later on,which means that the angle between Lamellae and x-axis is either of 0,90, 180 or 270 degrees), it is a set of images with different shifts indirection perpendicular to the Lamellae surface. In the case of “zeroangle”, there is no point in creating too wide images because ofperiodicity of the structure pattern, so the optimal width of simulatedTEM image has to cover the periodicity in corresponding direction plusmargins for more robust pattern matching. Typical margin in horizontaldirection is a half width of the measured TEM image width from eachside, so an optimal width of the simulated image for “zero angle” is theperiodicity in horizontal direction plus the width of measured TEMimage.

Then, pattern matching between measured and simulated TEM images isperformed (step 74). To this end, for each simulated TEM image from theset found in step 72, the best matching candidate is determined. A scoreof matching (merit function) is then calculated (step 76), e.g. usingthe Normalized Cross-Correlation technique, and a candidate with thebest matching score (minimal value of the merit function) is chosen(step 78). Coordinates of the best matching candidate are determined(step 80) and the Lamellae position is set accordingly (step 82).

Turning back to FIG. 3 , the optimization procedure (step 56) betweenthe simulated TEM image and measured TEM image (raw data) is performed,either using the above-described preceding rough positioning step 55 ornot. The optimization is actually a fitting procedure aimed at obtainingoptimized registration (best fit condition) of the simulated andmeasured images TEM_(simul) and TEM_(meas). The fitting procedureincludes a number of iterations while varying 3D model parameters(geometrical parameters) and/or TEM mode parameters/conditions (e.g.material properties and/or Lamellae position with respect to the modeledstructure) and recalculating the weight map, until the best fitcondition is achieved (step 58) for the following: the simulated TEMimage position in the 3D model, the TEM physical model parameters (TEMstack); 3D model parameters; and the required quality of the best fitbetween the TEM image and the 3D model. The resulting simulated TEMimage data, corresponding to the best fit condition is used to determinetherefrom one or more of the structure parameters. Actually, thistechnique, when repeated for various TEM images (Lamellae positions)provides for obtaining the full geometrical and possibly also materialinterpretation of the TEM image(s).

As described above, the registration (fitting) between the simulated andmeasured images is performed to match the relative position, shape andcontrast, and a matching score (merit function) is calculated whileperforming iterations to minimize the value of the merit function.Different merit functions (matching score schemes) can be used to definethe best fit condition. For example, Average Deviation procedurecompares intensities per pixel between the measured and simulatedimages. Another metrics may tolerate intensity gain and offsetvariations which may originate from electronic instabilities. Based oncalculated target function/merit function values, the regression isperformed on geometrical parameters and/or material properties and/orlamellae position and parameters, in order to find the best matchbetween a set of measured images and the corresponding predictions(simulation).

As described above, the simulated TEM image data includes one or moresimulated TEM images (created as described above) and a weight map basedon/determined by pre-defined data about the structure parts' map of thepatterned structure defining a specific device. In this connection,reference is made to FIGS. 5A to 5C exemplifying creation and use of theweight map.

FIG. 5A shows an exemplary patterned structure (stack) 140 which in thepresent example is configured to define a typical FinFET SiGe transistordevice. As shown, the device 140 has features/parts L₁ and L₂ ofdifferent types in the meaning of their effect of TEM images. Theseparts L₁ and L₂ of the transistor structure are made of SiGecompositions with, respectively, relatively low (about 15%) andrelatively high (about 30%) amount of Ge, and thus constitute partsrelating to, respectively, relatively weak and relatively strongparameters.

FIG. 5B shows a simulated TEM image (created as described above) for atheoretical structure similar to the structure 140, showing regions R₁in the TEM image corresponding to parts/features L₁.

FIG. 5C illustrates simulated weight map (image of weights) withareas/regions of enhanced weights which are connected to (correspond to)the structure's features L₁. As one can see from FIG. 5C, most of theTEM image area has weight W=1, but the regions of interest,corresponding to the structure's features L₁, with larger weight arelocated exactly around the areas/regions R₁. This is done in order toenhance the importance of TEM image matching at the areas L₁. It shouldbe noted that weights are assigned to different parts of the structure,and define, in the simulated TEM image, the weighted regions ofgeometrical- and/or material-related data.

As described above, structure parameter(s) can be determined using afitting procedure (iterative procedure) of comparing between thesimulated image data TEM_(simul) and measured data TEM_(meas), whilevarying the floating parameters of the simulated data, such as structureparameters, Lamellae geometry and position data, and the weight map,until a best fit condition is achieved (i e minimal value of meritfunction or target function). The weight map is re-calculated at eachstep of regression (iteration).

The following is the description of the use of the simulated TEM dataincluding the TEM image(s) and the weights' image to optimizedetermination of structure' parameters, including also weakparameter(s).

A merit function or target function (TF), defining a best fit conditionbetween measured and simulated TEM data, can be defined as follow (usingthe case of L2 metric/penalty of penalized regression):

${TF} = \frac{\sum{W_{IJ} \cdot \left( {S_{IJ} - M_{IJ}} \right)^{2}}}{\sum W_{IJ}}$

or as follows (in case of L1 metric of penalized regression):

${TF} = \frac{\sum{W_{IJ} \cdot {❘{S_{IJ} - M_{IJ}}❘}}}{\sum W_{IJ}}$

wherein W is the weight or weighting factor assigned to a structure'spart; and S and M correspond to simulated and measured images,respectively; and I and J are position related indexes that can coverthe whole area of the measured image. In the case when borders of themeasured TEM image are corrupted, the “Safe Margins” define an areawhich will be excluded from TF calculation (in both nominator anddenominator). Safe margins have 4 parameters: Left Margin, Right Margin,Top Margin and Bottom Margin. These Margins define the size of the image(from each side) which have to be excluded from the calculation. In thecase when the whole TEM image has good measurement, all above marginsare equal to zero, e.g. the whole image is used. In the case when Leftside of the TEM image is corrupted (for example, dark or brightartificial lines), then Left Margin is no-zero and defined accordinglyin order to exclude corrupted region.

Thus, here W_(IJ) is a weight (from the weight map image) for pixel (I,J), and S_(IJ) and M_(IJ) are simulated and measured TEM imagesrespectively. This “weighted” target function TF is used duringregression/iteration performed as described above.

Different weights are dynamically assigned to specific areas/regions ofthe TEM image connected to specific features/parts of the measuredstructure (measured semiconductor device). There are several possibleapproaches for dynamic calculation of weights, including for exampleStackMaker™ model with additionally defined meta-shapes (see below),assigning weights based on dissector material (see below), etc.

Considering the case of meta-shapes, they are not used for TEM imagesimulation, but for the weight map calculation. The meta-shapes has noeffect on calculation of simulated TEM image, but only used forcalculation of weight maps. Each meta-shape may have its own weights.The important feature of meta-shapes that their 3D size and 3D positionare linked to the Stack geometry data using an equation editor or macrosor intermediated parameters, and their locations can be adjustedautomatically to a desired feature/part when parameters of the Stack arechanged. For example, in L1 case, the meta-shapes at the form ofparallelepiped can be defined around L1. The center of theparallelepiped meta-shape can be linked to the center of L1 part, andthe size (in X, Y. and Z directions) of parallelepiped meta-shape canlinked to be larger than the size of L1 part (for example, twicelarger). The link of the size and position of meta-shapes to the partsof the structure is very important. For example, if the parameters ofthe Stack change, then both the region in the simulated weight maps andthe corresponding L1 region in the simulated TEM image will move at thesynchronized way.

Yet another option to define weight map is assigning the differentweight coefficient to different materials. This option is simpler foruser, because it does not require the modification of the existinggeometry. The weights per material assignment process is performedduring the recipe creation and includes assigning a weight to eachmaterial in dissector. At each step of regression, the weight map iscreated/updated by sampling the material inside the Lamellae cut. Theaccuracy of sampling for the weight map creation might be lower thanthat for the TEM image simulation.

In order to catch the material border at the TEM image regression, thearea of high weights is to be larger than the area of material itself.To this end, the area of higher weights' regions is expanded a littlebit (controlled by the recipe) on account of areas with lower weights.This can be implemented by non-linear filtering, for example usingmodification of typical dilation morphology algorithm from imageprocessing techniques.

In order to improve convergence, the weight map should preferably haveno abrupt variations. This condition can be achieved by smoothing theweight map from the previous regression step, for example by applying aGaussian Blur image processing algorithm.

It should be noted that the technique of the invention also provides forTEM recipe optimization. This can be carried out as follows: First,Lamella characteristics (thickness, position, orientation, etc.) aredefined for optimal information content based on the 3D model andsimulated TEM images. TEM measurement conditions (measurement mode, beamenergy, detector type,) are defined for optimal information contentbased on the 3D model and simulated TEM images. This allows forfacilitating interpretation of TEM and SEM images using the 3D model tooptimize measurement conditions (e.g. number of required TEM images).These procedures can be used for matching TEM image contours/edges tosimulated edges from the 3D model without physical TEM image model.

The TEM recipe optimization may also include weights optimization. Morespecifically, weights per material and/or per meta-shape can bepredefined during the recipe setup based on desired trade-off betweenthe accuracy of measurement of strong and weak parameters of interest.These weights (all weights or subset of weights or a single weight) canbe further optimized during regression. The optimization of weight canprovide leverage on the variation in the quality of measured TEM images.The optimization is performed within limits (each weight has its ownlimits—minimal and maximal values) which are defined during the recipesetup.

As described above, the data about the TEM measurement mode includingLamellae geometry data (e.g. Lamellae thickness, Lamellae orientation,Lamellae position with respect to the structure) and preferably alsodata about a detection scheme (BR, DF, ADF, HAADF or EL) correspondingto the measured TEM image data are used to determine the simulated TEMimage data. In some embodiments, a combination of two or more differentdetection schemes (measurement channels) are used.

The following is the description of an improved physical modeling,according to the present invention, for TEM images creation at HAADF andother modes (as schematically illustrated in FIG. 2D).

It should be noted that for a very thin Lamellae the attenuation ofchief/scanning e-beam within a Lamellae sample can be neglected. In thiscase HAADF (High Angle Annular Dark Field) or ADF (Annular Dark Field)mode/scheme in the TEM image simulation can be described by additivecontribution from different voxels on the beam's way as follows:

I _(HAADF) =I _(CHIEF) ·ΣB _(HAADF)(P)·D(p)

I _(ADF) =I _(CHIEF) ·ΣB _(ADF)(p)·D(p)

wherein B_(HAADF)(p) is an effective scattering efficiency from the p-thmaterial as measured by a HAADF detector, B_(ADF)(p) is an effectivescattering efficiency from the p-th material as measured by an ADFdetector, D (p) is a thickness of the p-th material along thepropagation/trajectory of the chief e-beam ray. The summation is done onthe way of the chief e-beam through the Lamellae sample.

As seen in FIG. 2D, the effective scattering cross section of thematerial is different for the TEM modes using HAADF and ADF detectionschemes because these include average over different collection angles.

However, this is not the case when the sample is not ultra-thin Lamellaesample or when TEM measurement mode with BF (Bright Field) detectionscheme is concerned.

In order to simulate TEM image for the BF mode, the attenuation of thechief scanning e-beam needs to be calculated as follows:

I _(BF) =I _(CHIEF)·exp(−ΣA _(BF)(p)·D(p))

wherein A_(BF)(p) is an attenuation coefficient of the chief ray for thep-th material, D(p) is a thickness of the p-th material on the way ofthe chief e-beam ray. The summation is done on the way of the chiefe-beam through the Lamellae sample.

The value of A_(BF)(p) is inversely proportional to the attenuationlength for the e-beam with specific energy at the p-th material.

In the case of non ultra-thin Lamellae sample, the above attenuationaffects the HAADF and ADF detected/measured signals as well:

$I_{HAADF} = {I_{CHIEF} \cdot {\int\limits_{0}^{D}{{B_{HADDF}(z)}{f(z)}{dz}}}}$$I_{ADF} = {I_{CHIEF} \cdot {\int\limits_{0}^{D}{{B_{ADF}(z)}{f(z)}{dz}}}}$

wherein D is a Lamellae thickness, B_(HAADF)(z) describes materialproperties and material's interaction with electron beam interaction(for HAADF detection scheme) at point z on the way of chief ray,B_(ADF)(z) describes material properties and material's interaction withelectron beam interaction (for ADF detection scheme) at point z on theway of chief ray, and f(z) describes the attenuation of the chief ray.

The integration is done over the path of the chief ray through theLamellae sample. The starting point (z=0) corresponds to the point wherethe chief e-beam enters the Lamellae sample, and the end point (z=D)corresponds to the point where the chief e-beam exist the Lamellaesample.

The attenuation of the chief beam, f(z), is calculated as follows:

${f(z)} = {\exp\left( {- {\int\limits_{0}^{z}{{A_{BF}(f)}{dt}}}} \right)}$

wherein A_(BF)(t) is an attenuation coefficient of the chief e-beam atpoint t (which depends on the material at this point).

The starting point (z=0) corresponds to the point where the chief e-beamenters the Lamellae sample, the larger z values correspond to deeperareas inside the Lamellae, z=D corresponds to the point where the chiefe-beam exist the Lamellae sample.

The above model can thus be used for determining the simulated TEMimage, which can then be used in the fitting procedure, e.g. optimizedby the use of simulated dynamic weight map, with respect to measured TEMimages to determine one or more parameters of the patterned structure.

Thus, the present invention provides for interpretation of the raw dataof TEM images, that cover a significant part of the process variationrange, to learn the process correlations between the structureparameters (geometry and material compositions). These processcorrelations may be then used to develop/optimize an OCD datainterpretation model, which incorporates these process correlations asconstraints in its parameterization. This allows/facilitates to obtainmore accurate OCD metrology by reducing effects of metrologycorrelations, and by ensuring that the OCD model represents the realprocess.

The present invention provides for utilizing the above described TEMdata interpretation to automatically obtain parameter values from TEMdata and use these parameters for optimizing measurements (e.g. measureddata interpretation) of other type, i.e. measurements based on differentphysical principles. An example of such other type measurements is OCDmetrology. The present invention provides for efficient OCD solutiondevelopment (model-based and modeless).

Examples of how the technique of the invention (full TEM datainterpretation) can contribute to the OCD metrology include:automatically obtaining parameter values from TEM and using them fordefense system (flagging situations where running OCD solution fails);automatically obtaining parameter values from TEM and using them forupdating a modeless OCD solution; deriving improved OCD models using theraw TEM images; using the raw TEM images to identify in-line failures ofOCD solution.

Combination of the TEM and OCD data (where the OCD data can be optimizedusing the TEM data as described above) can be used for furtheroptimization of a 3D model of the structure to extract geometricalparameters of the structure.

Also, the present invention provides for strict and global fitco-optimization of the TEM image data (one or more TEM images) with OCDspectral data (one or more spectra). To this end, a full set of imagesfrom Lamellae or only sub-set of images from Lamellae or single imagefrom Lamellae (and preferably also the weight map) can be used for theco-optimization. Similarly, a full set of available OCD spectra (withdifferent measurement conditions, e.g. different inclination/azimuth andpolarizations) or sub-set of available OCD spectra (for example, normalchannel TE and TM polarizations) or single OCD spectrum can be used foroptimization.

The strict co-optimization may utilize target function (merit function)which is an average of “local” merit functions from different sites (orsome weighted average of the merit functions from different sites). Therelevant structure parameters are kept (fixed) to be the same duringoptimization. In order to improve convergence, the co-optimization canbe done at two steps: at first step optimization procedure is performedwith regard to measurements on all sites independently, and at thesecond step the initial value of parameter(s) that is/are common for allthe sites is obtained as average of the optimization results from thefirst step. Then, all images are co-optimized simultaneously by keepingthe common parameters to have the same value.

Similarly, global co-optimization may use a target function which isaverage of target functions from different sites (or weighted average ofsuch target functions from different sites), and the relevant parametersare kept to be the same during optimization. However, the “common”values may slightly vary from site to site and from patterned region topattern region (e.g. die to die in a semiconductor wafer). In order totake into account such variation, the “common” parameters are keptindependent for each measurement site, but a penalty function is addedon variation of the “common” parameters. The penalty function is basedon at least one selected global parameter (behavior of global parameter)characterizing at least one property of the structure; this may beexpected physical behavior or expected process non-uniformity. Theprinciples of penalty functionbased data interpretation are described inUS 2013/124141, assigned to the assignee of the present application, andis incorporated herein by reference with respect to this specificexample.

For example, considering a Chemical Mechanical Planarization (CMP)process, the “bowl” behavior across the wafer is typical for CMP-relatedparameters. For such parameter at each step, the best fit parabola iscalculated (using parameter values and measurement sites positions onthe wafer):

V _(IDEAL) =a*r ² +b

Here, r is the wafer radius and V is the parameter, which is distributedaccording to a parabolic function along the wafer radius r.

Thereafter, the penalty function is calculated as variation from thebest fit parabolic behavior with some tolerance a of the processnon-uniformity:

$\delta = {\alpha\frac{1}{N}{\sum\left( \frac{V - {V_{IDEAL}(r)}}{\sigma} \right)^{2}}}$

In order to improve the convergence, the co-optimization can be done intwo steps, as described above, i.e., at the first step all sites areoptimized independently, and at the second step the penalty function isadded. Then, all these images are co-optimized simultaneously by usingthe penalty function on variation across the “common” parameters.

The following are some examples of the advantageous use of the techniqueof the invention, i.e. the full TEM data interpretation using a 3D modelof a structure.

One example is to use the TEM data obtained from one site forinterpreting TEM image of another site. In other words, parametersdetermined from TEM data of one site are injected to optimize TEMmeasurements on the other site. This may include injection of somevalues from another TEM-measured site on the same wafer (e.g. from thesame die or another die on the wafer), another structure at the same dieor another die on the wafer, the same structure at different Lamellaeorientation, the same structure at different Lamellae position, the samestructure at different Lamellae thickness. Alternatively, this may be“range” injection of some values from another TEM site on the same wafer(from the same die or another die on the wafer). Due to possiblevariations between different dies/sites of the wafer, the value isinjected but still remains open for optimization. The range for thisvalue is narrowed and centered around injected value. Similarly, theinjection may be performed for another structure at the same die oranother die on the wafer, the same structure at different Lamellaeorientation, the same structure at different Lamellae position, the samestructure at different Lamellae thickness.

Another possible examples of using the technique of the invention isstrict co-optimization of different TEM sites, i.e. TEM images fromdifferent sites (from the same die or another die on the wafer), e.g.another structure at the same die or another die on the wafer, the samestructure at different Lamellae orientation, the same structure atdifferent Lamellae position, the same structure at different Lamellaethickness. As described above, the co-optimization may use the targetfunction being an average (or weighted average) of local targetfunctions from different sites, and the relevant parameters are kept tobe the same during optimization. In order to improve the convergence,the co-optimization can be done at two steps: all sites are optimizedindependently, then the initial value of common parameters is obtainedas average of results from the independent optimization; and all imagesare co-optimized simultaneously by keeping common parameters to have thesame value.

Yet another example is global fit co-optimization of different TEMsites, i.e. TEM images from different sites (from the same die oranother die on the wafer). As described above, this may be anotherstructure at the same die or another die on the wafer, the samestructure at different Lamellae orientation, the same structure atdifferent Lamellae position, the same structure at different Lamellaethickness. As described above, the co-optimization may use targetfunction which is average (or weighted average) of target functions fromdifferent sites, and in order to take into account possible (slight)variation of the “common” values from site to site and from die to die,the “common” parameters are kept independent for each site, and thepenalty function (based on expected physical behavior or expectedprocess non-uniformity) is added on variation of the “common”parameters. This is exemplified above with respect to the CMP-relatedparameters. As also described above, the convergence can be improved bythe two-step optimization process: (1) all sites are optimizedindependently, (2) the penalty function is added, and all images areco-optimized simultaneously by using penalty function on variationacross “common” parameters.

Yet further example is the use of output parameters of a combined modelto calibrate a TEM tool. Let us consider a photo-lithography processwhich is proved to be extremely accurate (to the level of ppm's)concerning periodicity and period of the patterned structures. In caseof Double patterning, the process is accurate for Double-Pitch and inthe case of Quadro-Patterning it is accurate for Quadro-Pitch. PixelSize x can be optimized during a fitting procedure. Comparison of thepixel size x values obtained from optimization and from the settings ofthe TEM measurement mode can provide feedback on the accuracy of TEMsettings.

The TEM tool effects can be taken into account in the combined model.More specifically, Gain and Offset calibration curves of the electronicsof the tool can be incorporated in TEM data simulation in order toproduce exactly the TEM simulated image at exactly the same measurementconditions as the measured one. This is especially important fornon-linear effect, such as saturation which can be easily incorporatedin a simulation engine/module.

The present invention also provides for the process control (processdefense). To this end, the quality of the fitting/matching procedure(appropriately minimized merit function) can be used to automaticallyidentify the process faults/defects (mismatch from the 3D model of thestructure).

The structure parameters that can be determined from the fullinterpretation of the measured TEM images include any combination of thefollowing: geometrical parameters of the structure, for example CD,STI_HEIGHT, etc.; material characteristics, for example scatteringefficiency of Silicon or Silicon Oxide layers typically used insemiconductor wafers; Lamellae parameters, for example, Lamellaethickness and positions used in the TEM imaging/measurement; as well asacquisition parameters, for example gain and offset of amplifier usedfor STEM imaging, or pixel size of STEM or TEM image.

The quality of analysis of the measured TEM images is defined by thequality of the fitting/matching procedure and reliability of extractedparameters. The quality of fit is defined by the target function (meritfunction) value. If for example, the target function defined as averagedeviation, then lower values of the target function represent betterquality of analysis and higher reliability of extracted parametervalues. Also, the quality of fit may be defined by goodness of fit (GOF)value. This can be calculated as Normalized Crossed Correlation (NCC)between simulated and measured images. In this case, the higher valuesof GOF represents better quality of analysis and higher reliability ofextracted parameter values. The quality of fit may be defined byMiss-Fit Bias (MFB) value which represents the surface of miss-fitadjusted areas with the same sign of deviation of simulated frommeasured image. In this case, the larger values of MFB represent largersystematic bias between the modeled and actual structure, and meanslower reliability of extracted values. It should be noted that MFBindicates the limitation of the model from the real process. In otherwords, it indicates high probability of process faults or defects.

The 3D model of the structure (geometry and materials) is designed andoptimized in order to cover a normal process window of the manufacturingprocess and most common miss-process failures (or deviations from thenormal process. The process control (process defense) is a multi-levelprocedure.

First level includes extraction of the parameters' values, from eachLamellae image, based on the analysis of the TEM images. Also, thequality of analysis is calculated. This quality of analysis may havedifferent metrics. For example, if TEM measurements includes 7 TEMimages at different parts of Lamellae, each image can be analyzed, andset of parameters is extracted from each image. This includes one ormore of the following:

Lamellae average control: The parameters can be averaged, and theresulting values are compared with a normal process window range. If oneof the parameters goes beyond the normal process window range, an alarmis fired.

Strict control: If one of the parameters for one of the images (e.g.without averaging) goes beyond the normal process window range, an alarmis fired.

Within Lamellae variation control: The variation range within Lamellaefor each parameter can be calculated. It can be done by either using adifference between the maximal and minimal values, or by using 3StDevvalue. If the variation range for one of the parameters goes beyond thenormal process window range, an alarm is fired.

Target function average control: If the averaged value of the targetfunction from all TEM images becomes larger than a predefined threshold,an alarm is fired. The threshold for target function value can beobtained by investigating the target function values for the normalprocess window.

Target function strict control: If value of the target function from oneof the images becomes larger than a predefined threshold, an alarm isfired.

GOF average control: If the averaged value of GOF from all the imagesbecomes smaller than a predefined threshold, an alarm is fired. Thethreshold for GOF value can be obtained by investigating the GOF valuesfor the normal process window.

GOF strict control: If value of GOF from one of the images becomessmaller than a predefined threshold, an alarm is fired.

MFB average control: If the averaged value of MFB from all the imagesbecomes larger than a predefined threshold, an alarm is fired. Thethreshold for MFB value can be obtained by investigating the MFB valuesfor the normal process window.

MFB strict control: If the value of MFB from one of the images becomeslarger than a predefined threshold, an alarm is fired.

At the second level, the values of parameters are extracted by usingsimultaneous analysis of a full set or sub-set of TEM images inLamellae. For example, if Lamellae has 11 images, all 11 images can beused. Alternatively, only some of the images (e.g. 6 images from 11) canbe used. The reason to disregard other images may, for example, beassociated with low quality of the measured images. This procedureincludes one or more of the following:

Parameters control: The parameters values are compared with the normalprocess window range. If one of the parameters goes beyond the normalprocess window range, alarm is fired.

The target function control: If target function of simultaneousoptimization becomes larger than a predefined threshold, an alarm isfired. As described above, the threshold for the target function valuecan be obtained by investigating the target function values for thenormal process window.

GOF control: If GOF of simultaneous optimization becomes smaller than apredefined threshold (e.g. determined by investigating the GOF valuesfor the normal process window), an alarm is fired.

MFB control: If MFB of simultaneous optimization becomes larger than apredefined threshold (e.g. obtained by investigating the MFB values forthe normal process window), an alarm is fired.

A combined model can be used to calibrate out TEM tool effects.

Turning back to FIG. 1 , the TEM images analysis according to thetechnique of the invention can be used for optimizing measurements of adifferent type, e.g. optical measurements (e.g. OCD measurements). InFIG. 1 , model-based measurements of the different type are exemplified,where TEM data is used by modeling utility 22 and fitting module 24 tooptimize the data interpretation model and find the best fit conditionwith different type measured data, e.g. OCD data. Alternatively, oradditionally, such different type measurements may be model-based X-rayScatterometry (XRS) and/or model-based X-ray fluorescence (XRF). Inother words, structure parameters determined from the TEM imagesanalysis can be used for optimizing measured data interpretationmodel(s) for XRS and/or XRF measurements.

Also, the above-described automatic robust and accurate retrieval ofgeometric and/or material parameters of structures from one or severalTEM/STEM images for the purpose of Optical Proximity Correction (OPC)modeling, for semiconductor process development, for calibrating aprocess simulator.

What is claimed is:
 1. A control system for use in measuring one or moreparameters of a patterned structure, the control system being configuredas a computer system comprising: an input utility configured to receiveinput data comprising raw measured TEM image data, TEM_(meas), dataindicative of a TEM measurement mode; and a data processor configured toprocess the raw measured TEM image data, TEM_(meas), and generate outputdata indicative of one or more parameters of a patterned structure,wherein said data processor comprises: an optimization module configuredand operable to utilize said data indicative of the TEM measurement modeand perform a fitting procedure between the raw measured TEM image data,TEM_(meas), and predetermined simulated TEM image data, TEM_(simul), anddetermining one or more parameters of the structure from the simulatedimage data corresponding to a best fit condition, wherein saidpredetermined simulated TEM image data, TEM_(simul), being based on aparametrized three-dimensional model of features of the patternedstructure, and comprising one or more simulated TEM images and asimulated weight map comprising weights assigned to different regions inthe simulated TEM image corresponding to different features of thepatterned structure.